1. Field of the Invention
This invention relates generally to fault detection systems, and, more particularly, to methods and systems for analyzing process equipment processing variations using sensor data.
2. Description of the Related Art
After a complete reading of the present application, those skilled in the relevant art will understand that the present invention has broad application to a variety of industries involving the manufacture of a variety of different types of devices or workpieces. By way of example only, the background of the application will be discussed in the context of various problems encountered in the manufacture of integrated circuit devices. However, the present invention is not to be considered as limited to use only within the semiconductor manufacturing industry.
There is a constant drive within the semiconductor industry to increase the quality, reliability and throughput of integrated circuit devices, e.g., microprocessors, memory devices, and the like. This drive is fueled by consumer demands for higher quality computers and electronic devices that operate more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing the defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
Generally, a set of processing steps is performed on a group (lot) of wafers using a variety of process tools, including photolithography steppers, etch tools, deposition tools, polishing tools, thermal anneal process tools, implantation tools, etc. The technologies underlying semiconductor process tools have attracted increased attention over the last several years, resulting in substantial refinements. However, despite the advances made in this area, many of the process tools that are currently commercially available suffer certain deficiencies. In particular, some of such tools often lack advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format, as well as event logging, real-time graphical display of both current processing parameters and the processing parameters of the entire run, and remote, i.e., local site and worldwide, monitoring. These deficiencies can engender non-optimal control of critical processing parameters, such as throughput, accuracy, stability and repeatability, processing temperatures, mechanical tool parameters, and the like. This variability manifests itself as within-run disparities, run-to-run disparities and tool-to-tool disparities that can propagate into deviations in product quality and performance, whereas an ideal monitoring and diagnostics system for such tools would provide a means of monitoring this variability, as well as providing means for optimizing control of critical parameters.
One technique for improving the operation of a semiconductor processing line includes using a factory wide control system to automatically control the operation of the various process tools. The manufacturing tools communicate with a manufacturing framework or a network of processing modules. Each manufacturing tool is generally connected to an equipment interface. The APC system initiates a control script based upon a manufacturing model, which can be a software program that automatically retrieves the data needed to execute a manufacturing process. Often, semiconductor devices are staged through multiple manufacturing tools for multiple processes, generating data relating to the quality of the processed semiconductor devices.
During the fabrication process various events may take place that affect the performance of the devices being fabricated. That is, variations in the fabrication process steps result in device performance variations. Factors, such as feature critical dimensions, doping levels, contact resistance, particle contamination, etc., all may potentially affect the end performance of the device. Various tools in the processing line are controlled in accordance with performance models to reduce processing variation. Commonly controlled tools include photolithography steppers, polishing tools, etching tools, and deposition tools. Pre-processing and/or post-processing metrology data is supplied to process controllers for the tools. Operating recipe parameters, such as processing time, are calculated by the process controllers based on the performance model and the metrology information to attempt to achieve post-processing results as close to a target value as possible. Reducing variation in this manner leads to increased throughput, reduced cost, higher device performance, etc., all of which equate to increased profitability.
Target values for the various processes performed are generally based on design values for the devices being fabricated. For example, a particular process layer may have a target thickness. Operating recipes for deposition tools and/or polishing tools may be automatically controlled to reduce variation about the target thickness. In another example, the critical dimensions of a transistor gate electrode may have an associated target value. The operating recipes of photolithography tools and/or etch tools may be automatically controlled to achieve the target critical dimensions.
Typically, a control model is used to generate control actions for changing the operating recipe settings for a tool being controlled based on feedback or feedforward metrology data collected related to the processing by the tool. To function effectively, a control model must be provided with metrology data in a timely manner and at a quantity sufficient to maintain its ability to predict the future operation of the tool it controls.
Within many manufacturing industries great effort is made to insure that processing operations are performed accurately such that the resulting device meets target specifications. This is particularly true within the semiconductor manufacturing industry wherein many metrology tools and sensors are used to acquire a vast amount of metrology data to determine the effectiveness and accuracy of the processing operations performed in a tool and/or the compliance of the resulting workpiece with product specifications. Additionally, modern semiconductor manufacturing involves the use of various fault detection control routines and schemes to determine when a process operation or a particular tool is producing unacceptable results.
In one particularly illustrative example, in semiconductor manufacturing environments, it is desirable to be able to perform a desired process operation, e.g., a deposition process, on one of a plurality of such tools capable of performing the desired process operation. In general, having multiple tools available to perform a desired processing operation provides a greater manufacturing flexibility as it relates to producing the finished products. Such flexibility can increase the overall productivity and efficiency of the manufacturing facility.
To that end, it is sometimes assumed that performing a target process operation, i.e., a specific deposition recipe in each of a plurality of different process tools adapted to perform that function, will produce the same or very similar results, i.e., results within acceptable limits. In fact, in many cases, tool state data, e.g., temperatures, fluid or gas flows and flow rates, pressure, etc., is monitored for each of the plurality of process tools in an effort to insure that each of the individual process tools is individually capable of producing the desired finished product within product specifications. However, in practice, in performing a particular process operation, different process tools may produce very different results, even though the tool state data indicates that the tools should be producing similar results. As a more specific illustrative example, in performing a target deposition process operation that is intended to produce a 100 Å thick layer of silicon dioxide, a first deposition tool may form the layer to exactly 100 Å, with a surface planarity of approximately 95%, whereas a second deposition tool (performing the same deposition process recipe) may produce a layer of silicon dioxide having a thickness of 92 Å and a surface planarity of approximately 85%, while yet a third deposition tool (again performing the same deposition process recipe) may produce a layer having a thickness of approximately 89 Å and a surface planarity of approximately 80%. Such variations may occur even though the monitored tool state data indicates that each of the individual process tools should produce similar results. Such a phenomenon is sometimes referred to as a mismatch between process chambers or tools.
Confronted with such a situation, a manager of a semiconductor manufacturing facility does not, in fact, have several equivalent options for forming the layer of material. That is, in such a situation, the manufacturing flexibility of the facility is greatly reduced which may tend to reduce manufacturing efficiencies and product yields. When such situations occur, great efforts are made to determine the cause of such chamber mismatching as it can dramatically reduce manufacturing efficiencies and yields.
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.